Market Risk Measures Research Articles

Measuring Market Risk in Asset Management

Measuring Market Risk in Asset Management

A new Bayesian method for estimation of value at risk and conditional value at risk

AbstractValue at Risk (VaR) and Conditional Value at Risk (CVaR) have become the most popular measures of market risk in Financial and Insurance fields. However, the estimation of both risk measures is challenging, because it requires the knowledge of the tail of the distribution. Therefore, Extreme Value Theory initially seemed to be one of the best tools for this kind of problems, because using peaks-over-threshold method, we can assume the tail data approximately follow a Generalized Pareto distribution (GPD). The main objection to its use is that it only employs observations over the threshold, which are usually scarce. With the aim of improving the inference process, we propose a new Bayesian method that computes estimates built with all the information available. Informative prior Bayesian (IPB) method employs the existing relations between the parameters of the loss distribution and the parameters of the GPD that models the tail data to define informative priors in order to perform Metropolis–Hastings algorithm. We show how to apply IPB when the distribution of the observations is Exponential, stable or Gamma, to make inference and predictions. .Afterwards, we perform a thorough simulation study to compare the accuracy and precision of the estimates computed by IPB and the most employed methods to estimate VaR and CVaR. Results show that IPB provides the most accurate, precise and least biased estimates, especially when there are very few tail data. Finally, data from two real examples are analysed to show the practical application of the method.

ESTIMASI RISIKO PASAR PADA DATA RETURN KURS HARIAN DENGAN VALUE AT RISK MENGGUNAKAN MODEL–MODEL VOLATILITAS GARCH

Volatility is a critical tool in analyzing financial data. GARCH models are common tools widely use to forecast financial data volatility. This research discusses the measurement of market risk using the Value at Risk volatility model GARCH, IGARCH, and EGARCH for the USD major currency pairs which consist of seven currency pairs with the observation period July 2016 – September 2022. The results of the analysis show that the calculation of returns does not comply with the normal distribution, so the estimation of losses using the normal distribution VaR can be biased, instead this research used student’s t distribution error. The test results show that the IGARCH volatility model at a confidence level of 99% and 95% proves to be valid after the Kupiec test and Conditional Coverage Test are carried out on all tested currencies. Meanwhile, risk measurement using the EGARCH model is invalid on the EUR/USD, USD/CHF and AUD/USD exchange rates. Besides that, risk measurement using the SGARCH model is invalid on the EUR/USD and AUD/USD exchange rates.

Lost in noise? Some thoughts on the use of machine learning in financial market risk measurement

Machine learning has permeated almost all areas in which inferences are drawn from financial data. Nevertheless, in financial market risk measurement most machine learning techniques struggle with some inherent difficulties: Financial time series are very noisy, not stationary and mostly considerably short. This paper contains an easy to implement sequential learning algorithm that overcomes some of these disadvantages. It is based on a Kalman filtering mechanism for quite general stochastic processes and provides a first step in the direction of separating parameter dynamics from the ubiquitous noise component. The core idea here is to use some stylised facts inherent to financial markets time series such as time varying measures of volatility. The new approach is tested using real market data in two different settings. First, a hypothetical portfolio containing credit spread and equity risk is analysed over a time frame containing the outbreak of the global pandemic in 2020 and the beginning of the Russian attack on Ukraine in 2022. Another analysis is focused on US$/EUR exchange rate during a time span containing the global financial crisis of 2008 and the subsequent European sovereign crisis. In all test calculations the proposed sequential learning algorithm performs better than the historical simulation approach used by many firms in the banking industry to meet regulatory capital requirements. Due to its simplicity this method has a high degree of explainability and interpretability which will decrease the inherent model risk. The paper concludes with a discussion of model risk for machine learning in financial institutions. Compared to classical model risk frameworks, the emphasis must be put on the more prominent role of data. The simple approach described in this paper shows that machine learning in financial market risk does not have to get lost in noise.

Time point and scale measurement of carbon sink trading market risk based on catastrophe entropy and potential function

According to the principle of total entropy change of dissipative structure, the carbon trading market is defined as a nonlinear complex system that follows the law of entropy increase in this paper. Based on the potential function of sudden change theory, this paper studies the risk point and scale of the carbon trading market. The results show that (1) the theory of dissipative structure and catastrophe theory can be used as the theoretical basis of carbon financial market risk research, and its core technology can be used to measure and predict risks. (2) The risk mutation point measurement model based on the total entropy change principle and potential function technology effectively detected 16 major risk mutation points in the financial crisis, the European debt crisis, and the European new energy efficiency plan. The empirical test shows that the model has a good ability to capture abrupt changes and prediction accuracy. The fitting effect is very good. (3) The risk index value of the risk abrupt point can be calculated effectively by the risk scale measurement technique based on information entropy and the potential function surface equation. Furthermore, we judge the degree and grade of risk. From 2008 to 2021, amongst the 16 risk mutation points in the EU carbon trading market, there are three extremely high risk mutation points, seven high-risk mutation points, two medium-risk mutation points, two low-risk mutation points, and two very low risk mutation points. High risk or above grade accounted for 62.5%. Empirical analysis supports this conclusion.

International price earnings and country risk model in an Asian context

PurposeThis paper aims to estimate a statistical model of the country risk determination as represented by the country price earnings ratio (PE) to identify potentially mispriced countries. It uses the gross domestic product (GDP) growth rate and a dummy indicator for market-related events (i.e. financial crises), both approximating the business cycle. The model is used to compare a major Asian country’s (i.e. Japan) risk with Western countries’ risk.Design/methodology/approachThe model used finance variables such as the systemic, non-diversifiable, risk and foreign direct investments to characterize any country risk. A random effects model with panel data estimated the effects of macroeconomic and financial variables on PE. The simultaneity problem was checked using two stage least squares and some lagged independent variables.FindingsThe results explained to investors the country risk contributing factors: PE was positively correlated with variables that may increase dividends and market risk premia similar to GDP growth rates and total risk and negatively correlated with variables that increase market risk, namely, nominal risk-free interest rates and financial crises. Japan’s PE seemed to exceed most of the Western countries considered here, implying lower risks, lower interest rates and higher growth in the major Asian country Japan.Originality/valueThis paper focuses on the effectiveness of country risk measures in predicting periods of intense instability, similar to financial crises. This study contributes a model to measure market risk premium, using PE (or inversely, the earnings yield) as a proxy variable. Investors can use this risk measure in picking less risky stocks to include in their portfolio, calling for liberalizing Asian countries’ financial markets to improve their stock market capitalization.

Market risk, financial distress and firm performance in Vietnam.

In 2021, when the Covid-19 pandemic had a severe impact on the economy, a significant number of enterprises in Vietnam temporarily suspended doing business. Previous studies have focused on either model for predicting bankruptcy and financial distress or measuring market risk during extreme events. The effects of market risk and financial distress on a firm's performance have largely been ignored in the literature, particularly in Vietnam. This study examines the effects of market risk, measured using the conditional value-at-risk technique and financial distress proxied by the interest coverage ratio (ICR) on firm performance for 500 nonfinancial listed firms in Vietnam from 2012 to 2021. We also estimate the optimal ICR for Vietnam's listed firms. Two estimation techniques are used: dynamic panel models (two-step difference-and system-generalized method of moments) and panel threshold regression. We find that increased market risk reduces firm performance. However, a higher ICR (lower financial distress) also improves a firm's performance. With increased market risk, the financial performance of firms with a high ICR deteriorates significantly.

An Empirical Investigation of Value at Risk (VaR) Forecasting Based on Range-Based Conditional Volatility Models

Value at Risk (VaR) is a widely used measure of market risk. Precision in the estimation of volatility leads to accurate VaR forecasts. As volatility is time-varying and has a clustering effect, GARCH class of volatility models is helpful in modeling volatility more precisely. Studies have also shown that range-based volatility estimates are more efficient than traditional models that use only closing prices. Therefore, this study uses the GARCH family of volatility models to model and forecast VaR. The study compares conventional models that use closing prices alone, like GARCH and TARCH, with range-based models like RGARCH and RTARCH models, where the range defined as daily high price minus low price is introduced as an exogenous variable, to explore if the latter provides better predictive accuracy. All the models are back-tested using the Kupiec (1995) unconditional coverage and Christoffersen (1998) conditional coverage tests. The data period in the study ranges from 2003-2021, and we consider five BRICS indices and three major developed economies, namely, the USA, the UK, and Germany. An empirical investigation shows that range-based models do a better job in VaR forecasting as it has more information content than daily closing prices, thereby giving more accurate VaR estimates. The study hopes this finding will greatly help stakeholders like financial institutions, regulators, and practitioners in more effective market risk management.

Risk measurement and application of the international carbon market in the era of global conflict: A data-driven study using FCM

The accurate measurement and effective estimation of carbon market risk are crucial for practitioners and policymakers to mobilize resources toward the transition to a climate-resilient economy, particularly in a new era of global conflict. However, existing studies that have explored factors contributing to carbon market risk primarily relied on experience or subjective judgment in selecting risk-related factors. Such approaches undermine the estimation accuracy while making it difficult to ascertain causal inferences related to the risk spillover. To fill the gap, we adopted a data-driven factor analysis strategy by introducing the Fuzzy Cognitive Maps (FCM) model to establish a carbon market network and identify risk-related factors. We then evaluate the carbon market's risk level and spillover effects using combined econometric methods and explore their application in portfolio management. We report three main findings. First, based on our sample of 3217 observations between 2008 and 2022, five factors influencing carbon market risk emerged from the FCM, including OIL, COAL, SP500ENERGY, SPCLEANENERGY, and GPR. Second, we find a notable rise in risk spillover from GPR to EUA during the Russia-Ukraine conflict and an escalation of total cross-market spillover during extreme events. Third, our study presents new evidence on the hedging effect for EUA of the SP500ENERGY before the Russia-Ukraine conflict and of the SPCLEANENERGY during the conflict. Finally, implications are discussed for policymakers and investors.

Can market risk explain the systemic risk? Evidence from the US banking industry

PurposeThis paper sheds light on the impact of market risk measures on systemic risk. Market risk, which is captured by the volatility of stock market returns, is also decomposed into systematic and idiosyncratic risks.Design/methodology/approachThe author uses the five-factor asset pricing model and systemic risk methodologies to derive market and systemic risk measures, respectively. Using a sample of 2,667 US banks for over 30 years and employing panel data estimation techniques, the author tests the said relationship.FindingsIt is shown that idiosyncratic risk can surge systemic risk, while systematic risk plays a less important role. Results survive a battery of tests, including different systemic risk measures, controlling causality and interacting with bank size, market fear and crisis periods.Practical implicationsThese findings call for regulatory intervention, especially for large banks with high idiosyncratic risk.Originality/valueThis is the first paper that provides a more granular picture of the relationship between market and systemic risk from the US banking industry for more than 30 years.

Oil prices uncertainty, endogenous regime switching, and inflation anchoring

SummaryUsing a novel approach to model regime switching with dynamic feedback and interactions, we extract latent mean and volatility factors in oil price changes. We illustrate how the volatility factor constitutes a useful measure of oil market risk (or oil price uncertainty) for policy makers and analysts as it captures uncertainty not reflected in other economic/financial uncertainty measures. Then, in the context of a VAR, we investigate the role of oil price uncertainty in driving inflation expectations and inflation anchoring. We show that shocks to the mean factor lead to higher expected inflation and inflation disagreement among professional forecasters and households. In contrast, shocks to the volatility factor act as aggregate demand shocks in that they result in lower expected inflation, yet they do increase disagreement about future inflation among professional forecasters and, especially, among households. We also provide econometric evidence suggesting the proposed endogenous volatility switching model can outperform other regime switching models.

Integrating ESG risks into value-at-risk

This study addresses the regulatory request for sustainability-related risk integration into traditional financial risk measures. We propose a new risk metric that combines a traditional market risk measure expressed in terms of Value-at-Risk (VaR) and environmental, social, and governance (ESG) factors. The new metric, VaRESG, considers the orthogonality of the ESG criteria with fundamental variables, applying a perturbative approach and an entropy function of ESG factors. The pilot empirical application relies on a financial portfolio comprising approximately 3000 equities. The results show the predictive power of VaRESG to reduce unexpected losses (i.e., out-of-VaR). The results were confirmed especially under stress conditions, when the values of losses were higher (in terms of magnitude). To the best of our knowledge, this is the first attempt in the financial literature to effectively integrate ESG risks into the VaR measure to predict the expected losses of an equity portfolio. The measure of VaRESG can be useful to asset managers and institutional investors to reduce unexpected losses, and to supervisors interested in increasing the level of accuracy of VaR estimations.

From BASEL III to BASEL IV and beyond: Expected shortfall and expectile risk measures

The article contributes to the ongoing search for a market risk measure that is both coherent and elicitable. We compare two traditional measures, namely Value-at-Risk and the expected shortfall, with another relatively novel one established on the expectile probability term. Our research is based on five models: Black–Scholes, exponential tempered stable, Heston, Bates and another stochastic volatility model with a tempered stable jump correction. We apply the general Fourier inversion formula to derive closed form formulas for calculating not only the expectile based risk measure but also the Value-at-Risk and the expected shortfall. These models are calibrated by combining nonlinear programming with simulated annealing at a moving window. Additionally, we compare the generated values of the risk measures with the real ones. Last but not least, we modify the expectile based risk measure as well as the expected shortfall by introducing correction coefficients.

Forecasting extreme negative returns in gold and silver: A discrete‐duration approach to POT models

AbstractSudden and massive drops in precious metal prices severely impact the investment risk on these commodity markets. In this study, we examine the dynamics of extreme negative returns on gold and silver, as well as propose the discrete‐duration version of the autoregressive conditional duration peaks‐over‐threshold (ACD‐POT) model for measuring market risk. The model is tailored for the dynamics of extreme events in precious metal markets; this is because it can exhibit both the strong clustering of extreme‐event days, and the serial correlation in the magnitudes of extreme losses. From an econometric perspective, our approach complements the existing dynamic versions of POT models; this is so that the time interval (i.e., duration) between the extreme negative returns is treated not as a continuous variable, but as a discrete one. The discrete hazard function in our model represents the daily extreme loss event probability and it can be used to derive one‐day‐ahead forecasts of both the value at risk and expected shortfall for investments in gold and silver. Formal backtesting methods show that the discretized version of the POT model has a superior in‐sample fit and forecasting performance than the continuous‐duration POT models.

CBDC uncertainty: Financial market implications

The current state of the art in the central bank digital currency (CBDC) literature views indexes constructed from digital currency news to be fully informed about CBDC uncertainty and its impact on the financial system. We argue that the hedging behavior of participants in the currency futures market could be more informative than CBDC uncertainty news in the presence of limited risk absorption capacity in futures markets. We show that the hedging factor has a statistically significant effect on financial market risk aversion and measures of uncertainty. The hedging behavior of currency futures market participants is informative of agents' reactions to the news and central bank policies around CBDC. Our results also show that CBDC uncertainty is a significant risk transmitter in the financial system. Hence, this characteristic makes the hedging factor even more important because it can directly impact risk aversion via its moderating effects, which later influence CBDC uncertainty.

Assessing the importance of the choice threshold in quantifying market risk under the POT approach (EVT)

From a theoretical point of view, the selection of thresholds is a critical issue in the framework of the Peaks Over Threshold (POT) approach, which is why in the last decade numerous methodologies have been proposed for its selection. In this paper, we address this subject from an empirical point of view by assessing to what extent the selection of the threshold is decisive in quantifying the market risk. For measuring market risk, we use the Value at Risk (VaR) and the Expected Shortfall (ES) measures. The results obtained indicate that there is a large set of thresholds that provide similar Generalized Pareto Distribution (GPD) quantiles estimators and as a consequence similar market risk measures. Just only, for large thresholds, those corresponding to the 98th and 99th percentile of the GPD some differences are found. It means that the choice of threshold in the framework of the POT method may not be relevant in quantifying market risk when we use the VaR and ES measures for this task.

Beta Coefficient as a Measure of Market Risk in Assessing Investment Attractiveness

Beta Coefficient as a Measure of Market Risk in Assessing Investment Attractiveness

Quantification of the stock market value at risk by using FIAPARCH, HYGARCH and FIGARCH models

<abstract><p>The South African financial market is developing with periods of high and low volatility. Employing an adequate volatility model is essential to manage market risk. This research study was designed to investigate the effectiveness of the fractionally integrated asymmetric power autoregressive conditional heteroskedasticity contrasted with long-memory GARCH-type models, such as the fractionally integrated generalized autoregressive conditional heteroskedasticity and the hyperbolic generalized autoregressive conditional heteroskedasticity for producing the measure of market risk known as the value at risk. These long-memory GARCH-type models assume that the distributions of the index returns follow normal, student-$ t $, skewed student-$ t $ and generalized error distributions. The historical closing price time series of the Johannesburg Stock Exchange all share, the mining and the banking indices are considered. The value at risk and its backtesting for short and long trading positions on the different confident levels are computed and they correspond to the right and left quantiles of the return distributions, respectively. The results reveal that FIAPARCH with a standard student-$ t $ distribution is an appropriate model for producing a robust value at risk in the context of mining and banking indices. Alternatively, FIGARCH with the assumed skewed student-$ t $ distribution model is a good fit to produce a value at risk for the Johannesburg Stock Exchange All Share Index.</p></abstract>

A comparison of market risk measures from a twofold perspective: accurate and loss function

Under the new regulation based on Basel solvency framework, known as Basel III and Basel IV, financial institutions must calculate the market risk capital requirements based on the Expected Shortfall (ES) measure, replacing the Value at Risk (VaR) measure. In the financial literature, there are many papers dedicated to compare VaR approaches but there are few studies focusing in comparing ES approaches. To cover this gap, we have carried out a comprenhensive comparative of VaR and ES models applied to IBEX-35 stock index. The comparison has been carried out from a twofold perspective: accurate risk measure and loss functions. The results indicate that the method based on the conditional Extreme Value Theory (EVT) is the best in estimating market risk, outperforming Parametric method and Filter Historical Simulation.

Stock Market Volatility and Weak-form Efficiency: Evidence from an Emerging Market

There exists a vast literature on modeling and estimating aggregate stock market volatility over the past decade [e.g., Choudhry (1996); Mecagni and Sourial (1999) and Kabir, et al. (2000)]. Motivations for undertaking this exercise have been varied. Many value-at-risk models for measuring market risk require the estimation of volatility parameter. Portfolio diversifications and hedging strategies also require information on volatility as a key input. Volatility is defined as tendency of the assets price to fluctuate either up or down. Increased volatility is perceived as indicating a rise in financial risk which can adversely affect investor assets and wealth. It is observed that when stock market exhibit increased volatility there is a tendency on part of the investors to lose confidence in the market and they tend to exit the market. The nexus between volatility and economic fundamentals is still a moot point. Stock prices reflect information and quicker they are in absorbing accurately new information, more efficient is the stock market in allocating resources. The increase in volatility can be attributed to absorption of new information about economic fundamentals or some expectations about them. This kind of volatility is not harmful as there is no social cost associated with it. But if increased volatility is not explained by the level indicated by the fundamental economic factors, there is a tendency that stocks will be mispriced and this will lead to misallocation of resources [Karmaka (2006)].